New two-dimensional fuzzy C-means clustering algorithm for image segmentation |
| |
Authors: | ZHOU Xian-cheng SHEN Qun-tai LIU Li-mei |
| |
Affiliation: | [1]School of Information Science and Engineering, Central South University, Changsha 410083, China [2]School of Computer and Electronic Engineering, Hunan University of Commerce, Changsha 410205, China |
| |
Abstract: | To solve the problem of poor anti-noise performance of the traditional fuzzy C-means (FCM) algorithm in image segmentation,a novel two-dimensional FCM clustering algorithm for image segmentation was proposed.In this method,the image segmentation was converted into an optimization problem.The fitness function containing neighbor information was set up based on the gray information and the neighbor relations between the pixcls described by the improved two-dimensional histogram.By making use of the global searching ability of the predator-prey particle swarm optimization,the optimal cluster center could be obtained by iterative optimization,and the image segmentation could be accomplished.The simulation results show that the segmentation accuracy ratio of the proposed method is above 99%.The proposed algorithm has strong anti-noise capability,high clustering accuracy and good segment effect,indicating that it is an effective algorithm for image segmentation. |
| |
Keywords: | image segmentation fuzzy C-means clustering particle swarm optimization two-dimensional histogram |
本文献已被 CNKI 维普 万方数据 SpringerLink 等数据库收录! |
|